Predicting Provincial Gross Domestic Product Using Satellite Data and Machine Learning Methods: A Case Study of Thailand

Nattapong Puttanapong, Nutchapon Prasertsoong and Wichaya Peechapat
Additional contact information
Nutchapon Prasertsoong: Faculty of Economics, Thammasat University, Thailand
Wichaya Peechapat: Faculty of Economics, Thammasat University, Thailand

Asian Development Review (ADR), 2023, vol. 40, issue 02, 39-85

Abstract: This study introduced a new approach for monitoring regional development by applying satellite data with machine learning algorithms. Satellite data that represent physical features and environmental factors were obtained by developing a web-based application on the Google Earth Engine platform. Four machine learning methods were applied to the obtained geospatial data to predict provincial gross domestic product. The random forest method achieved the highest predictive performance, with 97.7% accuracy. The constructed random forest model was extended to conduct variable importance and minimal depth analyses, enabling the quantification of a factor’s influence on the prediction outcome. Variable importance and minimal depth analyses generated similar results, indicating that urban area and population are the most influential factors. Moreover, environmental and climate indicators exert medium-level effects. This study showed that integrating available satellite data and machine learning methods could be an alternative framework for facilitating a timely and costless monitoring system of regional development.

Keywords: Google Earth Engine; machine learning; regional development; satellite data; Thailand (search for similar items in EconPapers)
JEL-codes: C82 E01 O47 R11 R12 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0116110523400024
Open Access

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:adrxxx:v:40:y:2023:i:02:n:s0116110523400024

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0116110523400024

Access Statistics for this article

Asian Development Review (ADR) is currently edited by Tetsushi Sonobe

More articles in Asian Development Review (ADR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().